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Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

Author

Listed:
  • Mahdi Valikhan-Anaraki

    (Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran)

  • Sayed-Farhad Mousavi

    (Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran)

  • Saeed Farzin

    (Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran)

  • Hojat Karami

    (Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran)

  • Mohammad Ehteram

    (Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran)

  • Ozgur Kisi

    (School of Natural Sciences and Engineering, Ilia State University, Tbilisi 0162, Georgia)

  • Chow Ming Fai

    (Institute of Energy Infrastructure (IEI), Civil Engineering department, Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia)

  • Md. Shabbir Hossain

    (Department of Civil Engineering, Heriot-Watt University, Putrajaya 62200, Malaysia)

  • Gasim Hayder

    (Institute of Energy Infrastructure (IEI), Civil Engineering department, Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia)

  • Ali Najah Ahmed

    (Institute of Energy Infrastructure (IEI), Civil Engineering department, Universiti Tenaga Nasional (UNITEN), Selangor 43000, Malaysia)

  • Amr H. El-Shafie

    (Civil Engineering Department El-Gazeera High Institute for Engineering Al Moqattam, Cairo 11311, Egypt)

  • Huzaifa Bin Hashim

    (Civil Engineering Department, Faculty of Engineering; University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Haitham Abdulmohsin Afan

    (Civil Engineering Department, Faculty of Engineering; University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Sai Hin Lai

    (Civil Engineering Department, Faculty of Engineering; University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Ahmed El-Shafie

    (Civil Engineering Department, Faculty of Engineering; University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 10 6 m 3 , while the amount of water release based on the HA was 24.48 × 10 6 m 3 . Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.

Suggested Citation

  • Mahdi Valikhan-Anaraki & Sayed-Farhad Mousavi & Saeed Farzin & Hojat Karami & Mohammad Ehteram & Ozgur Kisi & Chow Ming Fai & Md. Shabbir Hossain & Gasim Hayder & Ali Najah Ahmed & Amr H. El-Shafie & , 2019. "Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2337-:d:224035
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    References listed on IDEAS

    as
    1. Hojat Karami & Sayed Farhad Mousavi & Saeed Farzin & Mohammad Ehteram & Vijay P. Singh & Ozgur Kisi, 2018. "Improved Krill Algorithm for Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3353-3372, August.
    2. Mohammad Ehteram & Hojat Karami & Saeed Farzin, 2018. "Reservoir Optimization for Energy Production Using a New Evolutionary Algorithm Based on Multi-Criteria Decision-Making Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2539-2560, May.
    3. Mohammad Azizipour & Vahid Ghalenoei & M. H. Afshar & S. S. Solis, 2016. "Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3995-4009, September.
    4. Y. Bolouri-Yazdeli & O. Bozorg Haddad & E. Fallah-Mehdipour & M. Mariño, 2014. "Evaluation of Real-Time Operation Rules in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 715-729, February.
    5. Mohammad Ehteram & Hojat Karami & Saeed Farzin, 2018. "Reducing Irrigation Deficiencies Based Optimizing Model for Multi-Reservoir Systems Utilizing Spider Monkey Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2315-2334, May.
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    Cited by:

    1. Mahdi Valikhan Anaraki & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2021. "Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 199-223, January.
    2. Mojtaba Kadkhodazadeh & Mahdi Valikhan Anaraki & Amirreza Morshed-Bozorgdel & Saeed Farzin, 2022. "A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods," Sustainability, MDPI, vol. 14(5), pages 1-37, February.

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